Adaptive Sequential Composition for Robot Behaviours
Benjamin Tam, Navinda Kottege, Nicolas Hudson, Michael Brünig
Abstract
Autonomous robots are prone to fail in real world environments, where unknown factors can cause their world model to be inaccurate or incomplete. This causes robots to become stuck or repeatedly perform unsuccessful actions believing it is the best option. Static switching frameworks such as sequential composition guarantee stability of the overall system if its constituents are also stable. However, due to unknown factors, robot actions may fail when the robot senses the environment inaccurately. We propose Adaptive Sequential Composition, a novel framework that dynamically selects robot behaviours based on their utility to achieving success. We show the usefulness of the framework in a simulated second order system task as well as its application in navigating a robot through narrow gaps, where the clearance with the gap is smaller than position accuracy. Simulated results show adaptive sequential composition outperforms sequential composition by up to 30 % when presented with unknown factors leading to behaviour failure. For navigating through a narrow gap, adaptive sequential composition improved success by 65 %.
BibTeX
@inproceedings{iros2022_adaptivesequenti,
title = {Adaptive Sequential Composition for Robot Behaviours},
author = {Benjamin Tam and Navinda Kottege and Nicolas Hudson and Michael Brünig},
booktitle = {IROS 2022},
year = {2022}
}